The present disclosure relates to systems and methods for medical imaging, such as magnetic resonance imaging and computed tomography. More particularly, the invention relates to systems and methods for reconstructing a medical image or a series of medical images.
Increasingly, clinical medicine employs medical imaging to drive clinical decisions and, in some instances, to direct therapeutic or surgical procedures. The non-invasive nature of medical imaging systems, such as magnetic resonance imaging (MRI), computed tomography (CT) imaging, and others, make these systems valuable sources of information about the patient and pathology and even physiology.
Regardless of the imaging system or the clinical purpose, the competing constraints of temporal and spatial resolution (and radiation dose in case of CT imaging) must be balanced with these systems. For example, MRI often has to contend with inherent acquisition speed limits to depict time-varying processes at the desired spatial resolution and coverage. Hence, incomplete sampling strategies followed by application of specialized image reconstruction algorithms have long been a popular strategy to increase effectual MRI speed. In dynamic MRI, such dedicated image reconstruction approaches often rely on various kinds of prior information in spatial and/or temporal dimensions to enhance reconstruction fidelity in these domains.
For example, the clinical need for high spatial and temporal resolution in time-resolved magnetic resonance applications often necessitates image reconstruction from incomplete datasets because the total scan time is limited. This is particularly the case when studying a physiological process because high temporal resolution is often required to acquire the desired information about the physiological process. One such strategy is referred to generally as “parallel imaging.” Parallel imaging techniques use spatial information from arrays of radio frequency (RF) receiver coils to compliment the encoding which would otherwise have to be obtained in a sequential fashion using RF pulses and field gradients (such as phase and frequency encoding). Each of the spatially independent receiver coils of the array carries certain spatial information in the form of a sensitivity profile. This information is utilized in order to achieve a complete location encoding by combining the simultaneously acquired coil data from the separate receiver coils. Specifically, parallel imaging techniques can reconstruct undersampled k-space, whereby the number of phase-encoded lines acquired is reduced by increasing the distance between these lines while keeping the maximal extent covered in k-space fixed. The combination of the separate NMR signals produced by the separate receiver coils enables a reduction of the acquisition time required for an image (in comparison to conventional k-space data acquisition) by a factor that, in the most favorable case, equals the number of the receiver coils. Thus, the use of multiple receiver coils acts to increase imaging speed by accelerating the encoding, without increasing gradient switching rates or RF power.
The advent of compressed sensing (CS) provided a new sub-Nyquist sampling requirement for images accepting a sparse representation in some basis. However, the limited spatial sparsity of magnetic resonance images affords only moderate acceleration factors before CS-based reconstructions introduce image blurring and blocky artifacts or other image errors.
Another group of reconstruction approaches employs a data-driven approach, in which low-resolution estimates of the dynamic image series are used to learn a low-rank temporal basis that can be used to represent all temporal behaviors in the time series either exactly or approximately. However, several problems exist in cases with complex temporal behavior of the underlying image series when high accelerations are needed. First, temporal behaviors present in the image series cannot be well represented by a small number of basis functions, while using a large number of basis functions at high accelerations does not improve conditioning of the reconstruction problem and results in amplified noise and/or unresolved aliasing artifacts. Second, there often is a large error stemming from learning the temporal basis from low-resolution images at high accelerations, which propagates into the reconstructed images. Third, higher spatial frequency information cannot be fully restored at high accelerations leading to loss of spatial resolution in the reconstructed images.
Therefore, it would be desirable to have a system and method for further addressing the challenge presented by the competing constraints of temporal resolution or radiation dose versus spatial resolution in medical imaging.